class_docstr: A PyTorch module that can be used to project from a dense layer
  to PyTorch distribution arguments.
  
  Parameters
  ----------
  in_features
      Size of the incoming features.
  dim_args
      Dictionary with string key and int value
      dimension of each arguments that will be passed to the domain
      map, the names are not used.
  domain_map
      Function returning a tuple containing one tensor
      a function or a nn.Module. This will be called with num_args
      arguments and should return a tuple of outputs that will be
      used when calling the distribution constructor.
class_docstr_tok: ["A","PyTorch","module","that","can","be","used","to","project","from","a","dense","layer","to","PyTorch","distribution","arguments",".","Parameters","--","--","--","--","--","in_features","Size","of","the","incoming","features",".","dim_args","Dictionary","with","string","key","and","int","value","dimension","of","each","arguments","that","will","be","passed","to","the","domain","map",",","the","names","are","not","used",".","domain_map","Function","returning","a","tuple","containing","one","tensor","a","function","or","a","nn.Module",".","This","will","be","called","with","num_args","arguments","and","should","return","a","tuple","of","outputs","that","will","be","used","when","calling","the","distribution","constructor","."]
code: def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
      params_unbounded = [proj(x) for proj in self.proj]
      return self.domain_map(*params_unbounded)
  
file_path: gluonts/src/gluonts/torch/distributions/output.py
imports: from typing import Callable, Dict, Optional, Tuple, Type
  import numpy
  import torch
  import torch.nn
  from gluonts.model.forecast_generator import ForecastGenerator
  from gluonts.torch.modules.lambda_layer import LambdaLayer
label_desc: Use image node_id 2 for calling the PtArgProj obj's underlying member method code with example usage: obj.forward(x) and returns: self